The PAKDD-10 Data Mining Competition results are in, and ensembles occupied the top 4 positions, and I think the top 5. The winner used Stochastic Gradient Boosting and Random Forests in Statistica, second place a combination of logistic regression and Stochastic Gradient Boosting (and Salford Systems CART for some feature extraction). Interestingly to me, the 5th place finisher used WEKA, an open source software tool.
The problem was credit risk with biased data for building the models, a good way to do the competition because this is the problem we usually face anyway: data was collected based on historic interactions with the company, biased by the approaches the company has used in the past rather than having a pure random sample to build models. Model performance was judged based on Area under the Curve (AUC), with the KS distance as the tie breaker (it's not everyday I hear folks pull out the KS distance!).
One submission in particular commented on the difference between how algorithms build models and the metric used to evaluate them. CART uses the Gini Index, Logistic regression the log-odds, Neural Networks minimize mean squared error (usually), none of which directly maximize AUC. But this topic is worthy of another post.